The grading of agarwood oil quality using k-Nearest Neighbor (k-NN)

Ismail, Nurlaila; Rahiman, Mohd Hezri Fazalul; Taib, Mohd Nasir; Ali, Nor Azah Mohd; Jamil, Muhammad; Tajuddin, Saıful Nizam

doi:10.1109/spc.2013.6735092

Cited by 9 publications

(8 citation statements)

References 34 publications

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“…Based on the score of the accuracy and results of performance measure, it can be concluded that the k classifier model using number of neighbours of 1 to 8 provide the best results for classification of agarwood oil quality with 100% classification accuracy score. The computation efficiency and accuracy score obtained from this paper is higher when compared to the previous paper of classifying agarwood oil using k-NN method in [16].…”

mentioning

confidence: 67%

“…Some of the literatures are [13][14][15][16]. In [13], it carried out a research on predicting the yield of crops by using machine learning models.…”

Section: Machine Learning In Agriculture Fieldmentioning

confidence: 99%

“…In [16], the author uses k-Nearest Neighbor (k-NN) approach to grade the quality of agarwood oil. The k-Nearest Neighbor is chosen because of its simplicity and efficiency.…”

Section: A Abas Et Al J Fundam Appl Sci 2017 9(4s) 62-76 66mentioning

confidence: 99%

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Agarwood oil quality classifier using machine learning

Abas¹,

Zubir²,

Ismail³

et al. 2018

J. Fundam and Appl Sci.

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mentioning

confidence: 67%

“…Some of the literatures are [13][14][15][16]. In [13], it carried out a research on predicting the yield of crops by using machine learning models.…”

Section: Machine Learning In Agriculture Fieldmentioning

confidence: 99%

See 1 more Smart Citation

Agarwood oil quality classifier using machine learning

Abas¹,

Zubir²,

Ismail³

et al. 2018

J. Fundam and Appl Sci.

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“…The best regression line of hidden neurons will be identified to discriminate the quality of Gaharu oil from high to low quality. Levenberg Marquadt (LM) algorithm was implemented for the trained dataset because it is the most commonly used optimization algorithm in many research studies [13], [38], [39]. The findings strongly showed that a best fit linear regression line with a value of R exactly 1 at hidden neurons number 2 which is the lowest compared to other neurons [40].…”

Section: Quality Grading System Of Essential Oilsmentioning

confidence: 99%

A Ppreliminary study on the intelligent model of k-nearest neighbor for agarwood oil quality grading

Huzir

Mahabob

Amidon

et al. 2022

IJEECS

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Essential <span>oils extracted from trees has various usages like perfumes, incense, aromatherapy and traditional medicine which increase their popularity in global market. In Malaysia, the recognition system for identifying the essential oil quality still does not reach its standard since mostly graded by using human sensory evaluation. However, previous researchers discovered new modern techniques to present the quality of essential oils by analyse the chemical compounds. Agarwood essential oil had been chosen for the proposed integrated intelligent models with the implementation of k-nearest neighbor (k-NN) due to the high demand and an expensive natural raw world resource. k-NN with Euclidean distance metrics had better performance in terms of its confusion matrix, sensitivity, precision accuracy and specificity. This paper presents an overview of essential oils as well as their previous analysis technique. The review on k-NN is done to prove the technique is compatible for future research studies based on its performance.</span>

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“…In case of utilizing code-aided synchronization, typically 8 outer iterations are performed (see Fig. 1) to achieve the desired communications performance as reported in Ali et al (2014). In such system, the demapper must deliver a throughput of 8-times 20 Msymbols/second.…”

Section: Introductionmentioning

confidence: 99%

A high throughput architecture for a low complexity soft-output demapping algorithm

2015

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Abstract. Iterative channel decoders such as Turbo-Code and LDPC decoders show exceptional performance and therefore they are a part of many wireless communication receivers nowadays. These decoders require a soft input, i.e., the logarithmic likelihood ratio (LLR) of the received bits with a typical quantization of 4 to 6 bits. For computing the LLR values from a received complex symbol, a soft demapper is employed in the receiver. The implementation cost of traditional soft-output demapping methods is relatively large in high order modulation systems, and therefore low complexity demapping algorithms are indispensable in low power receivers. In the presence of multiple wireless communication standards where each standard defines multiple modulation schemes, there is a need to have an efficient demapper architecture covering all the flexibility requirements of these standards. Another challenge associated with hardware implementation of the demapper is to achieve a very high throughput in double iterative systems, for instance, MIMO and Code-Aided Synchronization. In this paper, we present a comprehensive communication and hardware performance evaluation of low complexity soft-output demapping algorithms to select the best algorithm for implementation. The main goal of this work is to design a high throughput, flexible, and area efficient architecture. We describe architectures to execute the investigated algorithms. We implement these architectures on a FPGA device to evaluate their hardware performance. The work has resulted in a hardware architecture based on the figured out best low complexity algorithm delivering a high throughput of 166 Msymbols/second for Gray mapped 16-QAM modulation on Virtex-5. This efficient architecture occupies only 127 slice registers, 248 slice LUTs and 2 DSP48Es.

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The grading of agarwood oil quality using k-Nearest Neighbor (k-NN)

Cited by 9 publications

References 34 publications

Agarwood oil quality classifier using machine learning

Agarwood oil quality classifier using machine learning

A Ppreliminary study on the intelligent model of k-nearest neighbor for agarwood oil quality grading

A high throughput architecture for a low complexity soft-output demapping algorithm

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